From 40e224c0d95570a8615106a3ff77acf83026e6be Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <maltanar@gmail.com>
Date: Wed, 25 Mar 2020 11:18:52 +0000
Subject: [PATCH] [Test] use model later on in transforms as golden for
cnv-w1a1
---
.../test_convert_to_hls_layers_cnv.py | 17 ++++++++---------
1 file changed, 8 insertions(+), 9 deletions(-)
diff --git a/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py b/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
index e2df959dd..6345c93fb 100644
--- a/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
+++ b/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
@@ -64,7 +64,14 @@ def test_convert_to_hls_layers_cnv_w1a1():
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
model = model.transform(Streamline())
- model.save("cnv-streamline.onnx")
+ model = model.transform(LowerConvsToMatMul())
+ model = model.transform(MakeMaxPoolNHWC())
+ model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
+ model = model.transform(ConvertBipolarMatMulToXnorPopcount())
+ model = model.transform(absorb.AbsorbAddIntoMultiThreshold())
+ model = model.transform(absorb.AbsorbMulIntoMultiThreshold())
+ model = model.transform(RoundAndClipThresholds())
+ model.save("golden.onnx")
# load one of the test vectors
fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
input_tensor = np.load(fn)["arr_0"].astype(np.float32)
@@ -74,15 +81,7 @@ def test_convert_to_hls_layers_cnv_w1a1():
expected_ctx = oxe.execute_onnx(model, input_dict, True)
expected = expected_ctx[model.graph.output[0].name]
- model = model.transform(LowerConvsToMatMul())
- model = model.transform(MakeMaxPoolNHWC())
- model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
- model = model.transform(ConvertBipolarMatMulToXnorPopcount())
- model = model.transform(absorb.AbsorbAddIntoMultiThreshold())
- model = model.transform(absorb.AbsorbMulIntoMultiThreshold())
- model = model.transform(RoundAndClipThresholds())
model = model.transform(to_hls.InferBinaryStreamingFCLayer())
-
for node in model.graph.node:
if node.op_type == "StreamingFCLayer_Batch":
inst = getCustomOp(node)
--
GitLab